What problem does it solve? Writing data validation logic by hand leads to brittle manual parsing, inconsistent type coercion, and missed edge cases when handling external API responses or user input. This Skill provides Pydantic v2 patterns that replace ad-hoc dict parsing with declarative, type-safe model validation. ## Core Features & Use Cases - Native v2 Validation: Uses model_validate, field_validator, and model_validator instead of manual parsing functions. - External Data Mapping: Handles mismatched field names with Field aliases and ORM objects with from_attributes. - Advanced Modeling: Covers nested models, discriminated unions for variant types, computed fields, strict mode, and serialization controls. - Use Case: When integrating a third-party REST API that returns camelCase JSON, define a Pydantic model with aliases and validators so responses are parsed, type-checked, and normalized in one model_validate call. ## Quick Start Review my Pydantic models in models.py and refactor any manual parsing into proper Pydantic v2 validation.
